Abstract

Classifying action videos became challenging problem
in computer vision community. In this work, action videos are
represented by dictionaries which are learned by online dictionary
learning (ODL). Here, we have used two simple measures
to classify action videos, reconstruction error and projection.
Sparse approximation algorithm LASSO is used to reconstruct
test video and reconstruction error is calculated for each of the
dictionaries. To get another discriminative measure projection,
the test vector is projected onto the atoms in the dictionary.
Minimum reconstruction error and maximum projection give
information regarding the action category of the test vector. With
action bank as a feature vector, our best performance is 59.3%
on UCF50 (benchmark is 57.9%), 97.7% on KTH (benchmark
is 98.2%)and 23.63% on HMDB51 (benchmark is 26.9%).